Purchasing Power to the People: An Agent-Based Simulation of Pandemic Economic Recovery

2021 ◽  
pp. 87-112
Author(s):  
Youngsun Hwang ◽  
Joseph Immormino ◽  
Glenn-Iain Steinback
Keyword(s):  
Economic Recovery ◽  
Purchasing Power ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The People

Process Estimation of Word-of-Mouth Information Spread Based on Ad Hoc Communications

2012 ◽  
Vol 16 (5) ◽  
pp. 619-630
Author(s):  
Daisuke Katagami ◽  
◽  
Mizuki Takei ◽  
Katsumi Nitta ◽  
Keyword(s):  
Time Series ◽  
Word Of Mouth ◽  
Ad Hoc ◽  
Cluster Structure ◽  
Simulation Method ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Transmitted Information ◽  
The People ◽  
Information Spread

We focus on the information spread in ad hoc communications, and propose a method of estimating process of word-of-mouth information spread based on analysis of the human network generated by using a contact history among people. This method extracts the cluster structure of people which changes according to the time-series and identifies the clusters including the people which transmitted information. The results of the experiments which applied the proposal method to the data generated by using an agent based simulation method shows that it becomes possible to estimate the information spread process from a connection among the clusters in the human network.

scholarly journals Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach

2020 ◽  
Author(s):  
Oguzhan Alagoz ◽  
Ajay K. Sethi ◽  
Brian W. Patterson ◽  
Matthew Churpek ◽  
Nasia Safdar
Keyword(s):  
New York ◽  
Simulation Model ◽  
Disease Transmission ◽  
Major Effect ◽  
Agent Based Simulation ◽  
Population Demographics ◽  
Agent Based ◽  
Social Distancing ◽  
The People ◽  
The Impact

ABSTRACTBackgroundAcross the U.S., various social distancing measures were implemented to control COVID-19 pandemic. However, there is uncertainty in the effectiveness of such measures for specific regions with varying population demographics and different levels of adherence to social distancing. The objective of this paper is to determine the impact of social distancing measures in unique regions.MethodsWe developed COVid-19 Agent-based simulation Model (COVAM), an agent-based simulation model (ABM) that represents the social network and interactions among the people in a region considering population demographics, limited testing availability, imported infections from outside of the region, asymptomatic disease transmission, and adherence to social distancing measures. We adopted COVAM to represent COVID-19-associated events in Dane County, Wisconsin, Milwaukee metropolitan area, and New York City (NYC). We used COVAM to evaluate the impact of three different aspects of social distancing: 1) Adherence to social distancing measures; 2) timing of implementing social distancing; and 3) timing of easing social distancing.ResultsWe found that the timing of social distancing and adherence level had a major effect on COVID-19 occurrence. For example, in NYC, implementing social distancing measures on March 5, 2020 instead of March 12, 2020 would have reduced the total number of confirmed cases from 191,984 to 43,968 as of May 30, whereas a 1-week delay in implementing such measures could have increased the number of confirmed cases to 1,299,420. Easing social distancing measures on June 1, 2020 instead of June 15, 2020 in NYC would increase the total number of confirmed cases from 275,587 to 379,858 as of July 31.ConclusionThe timing of implementing social distancing measures, adherence to the measures, and timing of their easing have major effects on the number of COVID-19 cases.Primary Funding SourceNational Institute of Allergy and Infectious Diseases Institute

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